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primary topic: SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK
type: Thesis
type: Article
abstract: Penelitian ini bertujuan mengembangkan sistem deteksi kecelakaan lalu lintas dan tindakan kriminal bersenjata menggunakan model deep learning YOLOv11 yang terintegrasi dengan platform web berbasis Flask. Sistem dirancang untuk memberikan notifikasi real-time guna meningkatkan respons unit tanggap darurat. Metode penelitian menggunakan Research and Development (R&D) dengan pendekatan Waterfall yang meliputi pengumpulan dataset hybrid, pra-pemrosesan, pelatihan model YOLOv11n, serta pengembangan dan integrasi sistem web. Dataset terdiri dari 2.301 gambar hasil augmentasi yang dibagi menjadi 85% pelatihan, 10% validasi, dan 5% pengujian. Hasil evaluasi model menunjukkan kinerja tinggi dengan precision 97,1%, recall 98,2%, mAP50 98,9%, dan mAP50-95 87,9%. Pada pengujian sistem berbasis web, video lokal mencapai akurasi 91,11%, kecepatan deteksi rata-rata 61,14 ms, frame rate 8,66 FPS, dan latensi 63,4 ms. Pada IP CCTV real-time, akurasi mencapai 64,44%, kecepatan deteksi 65,72 ms, frame rate 6,57 FPS, dan latensi total 119,1 ms. Sistem alert berhasil memicu notifikasi suara dan menyimpan bukti kejadian dengan akurasi 100%. Kesimpulan penelitian menegaskan efektivitas sistem dalam mendeteksi kejadian darurat secara cepat, meskipun perlu pengembangan dataset lebih variatif dan optimasi infrastruktur komputasi untuk meningkatkan kinerja real-time. This research aims to develop a road accident detection system using the YOLOv11 deep learning model integrated with a Flask-based web platform. The system is designed to provide real-time notifications to improve emergency response unit responsiveness. The research methodology employs Research and Development (R&D) with a Waterfall approach, encompassing hybrid dataset collection, pre-processing, YOLOv11n model training, and web system development and integration. The dataset consists of 2,301 augmented images divided into 85% for training, 10% for validation, and 5% for testing. Model evaluation results demonstrate high perform…
date: 2025-07-30
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
has document: SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
subject: L Education (General)
subject: LB Theory and practice of education
subject: T Technology (General)

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document
has file: S_TE_2108071_Title.pdf
has part: S_TE_2108071_Title.pdf

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document
has file: S_TE_2108071_Chapter1.pdf
has part: S_TE_2108071_Chapter1.pdf

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document
has file: S_TE_2108071_Chapter3.pdf
has part: S_TE_2108071_Chapter3.pdf

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document
has file: S_TE_2108071_Chapter5.pdf
has part: S_TE_2108071_Chapter5.pdf

SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK (Text)
type: Document

Universitas Pendidikan Indonesia
type: Organization

KODEPRODI83201#Pendidikan Teknik Elektro_S1, Universitas Pendidikan Indonesia
type: Organization

- Arjuni Budi Pantjawati
type: Person
given name: -
family name: Arjuni Budi Pantjawati

- Muhammad Imron Maulana
type: Person
given name: -
family name: Muhammad Imron Maulana

HTML Summary of #144853 SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK
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primary topic: SISTEM DETEKSI ROAD ACCIDENT BERBASIS WEB MENGGUNAKAN MODEL DEEP LEARNING YOLOV11 DAN FRAMEWORK FLASK

L Education (General)
type: Concept

LB Theory and practice of education
type: Concept

T Technology (General)
type: Concept